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Optically anisotropic materials are sought after for tailoring the polarization of light. Recently, colossal optical anisotropy (Δn = 2.1) was reported in a quasi-one-dimensional chalcogenide, Sr9/8TiS3. Compared to SrTiS3, the excess Sr in Sr9/8TiS3 leads to periodic structural modulations and introduces additional electrons, which undergo charge ordering on select Ti atoms to form a highly polarizable cloud oriented along the c-axis, hence resulting in the colossal optical anisotropy. Here, further enhancement of the colossal optical anisotropy to Δn = 2.5 in Sr8/7TiS3 is reported through control over the periodicity of the atomic-scale modulations. The role of structural modulations in tuning the optical properties in a series of SrxTiS3 compounds with x = [1, 9/8, 8/7, 6/5, 5/4, 4/3, 3/2] is investigated using density-functional-theory (DFT) calculations. The structural modulations arise from various stacking sequences of face-sharing TiS6 octahedra and twist-distorted trigonal prisms and are found to be thermodynamically stable for 1 < x < 1.5. As x increases, an indirect-to-direct band gap transition is predicted for x ≥ 8/7 along with an increased occupancy of Ti-dz2 states. Together, these two factors result in a theoretically predicted maximum birefringence of Δn = 2.5 for Sr8/7TiS3. Single crystals of Sr8/7TiS3 were grown using a molten-salt flux method. Single-crystal X-ray diffraction measurements confirm the presence of long-range order with a periodicity corresponding to Sr8/7TiS3, which is further corroborated by atomic-scale observations using scanning transmission electron microscopy. Polarization-resolved Fourier-transform infrared spectroscopy of Sr8/7TiS3 crystals shows Δn ≈ 2.5, in excellent agreement with the theoretical predictions. Overall, these findings demonstrate the compositional tunability of optical properties in SrxTiS3 compounds by control over atomic scale modulations and suggest that similar strategies could be extended to other compounds having modulated structures. 

Abstract BaTiS₃, a quasi-1D complex chalcogenide, has gathered considerable scientific and technological interest due to its giant optical anisotropy and electronic phase transitions. However, the synthesis of high-quality BaTiS₃crystals, particularly those featuring crystal sizes of millimeters or larger, remains a challenge. Here, we investigate the growth of BaTiS₃crystals utilizing a molten salt flux of either potassium iodide, or a mixture of barium chloride and barium iodide. The crystals obtained through this method exhibit a substantial increase in volume compared to those synthesized via the chemical vapor transport method, while preserving their intrinsic optical and electronic properties. Our flux growth method provides a promising route toward the production of high-quality, large-scale single crystals of BaTiS₃, which will greatly facilitate advanced characterizations of BaTiS₃and its practical applications that require large crystal dimensions. Additionally, our approach offers an alternative synthetic route for other emerging complex chalcogenides. Graphical Abstract 

Abstract Vapor‐pressure mismatched materials such as transition metal chalcogenides have emerged as electronic, photonic, and quantum materials with scientific and technological importance. However, epitaxial growth of vapor‐pressure mismatched materials are challenging due to differences in the reactivity, sticking coefficient, and surface adatom mobility of the mismatched species constituting the material, especially sulfur containing compounds. Here, we report a novel approach to grow chalcogenides – hybrid pulsed laser deposition – wherein an organosulfur precursor is used as a sulfur source in conjunction with pulsed laser deposition to regulate the stoichiometry of the deposited films. Epitaxial or textured thin films of sulfides with variety of structure and chemistry such as alkaline metal chalcogenides, main group chalcogenides, transition metal chalcogenides and chalcogenide perovskites are demonstrated, and structural characterization reveal improvement in thin film crystallinity, and surface and interface roughness compared to the state‐of‐the‐art. The growth method can be broadened to other vapor‐pressure mismatched chalcogenides such as selenides and tellurides. Our work opens up opportunities for broader epitaxial growth of chalcogenides, especially sulfide‐based thin film technological applications. This article is protected by copyright. All rights reserved 

Abstract Polarimetric infrared (IR) detection bolsters IR thermography by leveraging the polarization of light. Optical anisotropy, i.e., birefringence and dichroism, can be leveraged to achieve polarimetric detection. Recently, giant optical anisotropy is discovered in quasi‐1D narrow‐bandgap hexagonal perovskite sulfides, A_1+xTiS₃, specifically BaTiS₃and Sr_9/8TiS₃. In these materials, the critical role of atomic‐scale structure modulations in the unconventional electrical, optical, and thermal properties raises the broader question of the nature of other materials that belong to this family. To address this issue, for the first time, high‐quality single crystals of a largely unexplored member of the A_1+xTiX₃(X = S, Se) family, BaTiSe₃are synthesized. Single‐crystal X‐ray diffraction determined the room‐temperature structure with theP31cspace group, which is a superstructure of the earlier reportedP6₃/mmcstructure. The crystal structure of BaTiSe₃features antiparallelc‐axis displacements similar to but of lower symmetry than BaTiS₃, verified by the polarization dependent Raman spectroscopy. Fourier transform infrared (FTIR) spectroscopy is used to characterize the optical anisotropy of BaTiSe₃, whose refractive index along the ordinary (E⊥c) and extraordinary (E‖c) optical axes is quantitatively determined by combining ellipsometry studies with FTIR. With a giant birefringence Δn∼ 0.9, BaTiSe₃emerges as a new candidate for miniaturized birefringent optics for mid‐wave infrared to long‐wave infrared imaging. 

Abstract Predicting assay results for compounds virtually using chemical structures and phenotypic profiles has the potential to reduce the time and resources of screens for drug discovery. Here, we evaluate the relative strength of three high-throughput data sources—chemical structures, imaging (Cell Painting), and gene-expression profiles (L1000)—to predict compound bioactivity using a historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity for 6–10% of assays, and in combination they predict 21% of assays with high accuracy, which is a 2 to 3 times higher success rate than using a single modality alone. In practice, the accuracy of predictors could be lower and still be useful, increasing the assays that can be predicted from 37% with chemical structures alone up to 64% when combined with phenotypic data. Our study shows that unbiased phenotypic profiling can be leveraged to enhance compound bioactivity prediction to accelerate the early stages of the drug-discovery process. 

Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data. Here, we present an improved strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. We use weakly supervised learning for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN. We evaluated our strategy on three publicly available Cell Painting datasets, and observed that the Cell Painting CNN improves performance in downstream analysis up to 30% with respect to classical features, while also being more computationally efficient.